Results obtained by the computer algorithm’s assessment were compared using three radiologists’ visual breast-density ratings.

‘The computer ratings yielded a strong correlation with the reader’s overall ratings, suggesting that the algorithm correlated highly with the ratings of our human observers, although we noted that human observers tended to consistently underestimate overall breast density,’ said Dr Naomi Saenz, who carried out the study.

‘Breast density has been shown to be an important risk factor for breast cancer. It is evaluated visually and subjectively in current practice and we found that assessment of breast density can vary from one radiologist to the next.’

The algorithm automatically segments the breast tissue from its background, removes the pectoral muscle and then determines the borders of the areas of the breast that are dense, which it then separates from other breast tissue and fat.

‘This method may help physicians give more objective and accurate recommendations on who will need further imaging or who might not, as well as reduce variability from one reader to the next.

‘It could easily be implemented into a picture archiving and communications system (PACS) or a mammographic reading station so that the radiologist would have this information on hand while reading and dictating,’ said Saenz.

For a copy of the full study, please contact Heather Curry via email at hcurry@arrs.org.

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